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Eley: On the Effectiveness of Burst Buffers for Big Data Processing in HPC Systems

  • Orcun Yildiz
  • , Amelie Chi Zhou
  • , Shadi Ibrahim*
  • *Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

17 Citations (Scopus)

Abstract

Burst Buffer is an effective solution for reducing the data transfer time and the I/O interference in HPC systems. Extending Burst Buffers (BBs) to handle Big Data applications is challenging because BBs must account for the large data inputs of Big Data applications and the performance guarantees of HPC applications - which are considered as first-class citizens in HPC systems. Existing BBs focus on only intermediate data of Big Data applications and incur a high performance degradation of both Big Data and HPC applications. We present Eley, a burst buffer solution that helps to accelerate the performance of Big Data applications while guaranteeing the performance of HPC applications. In order to improve the performance of Big Data applications, Eley employs a prefetching technique that fetches the input data of these applications to be stored close to computing nodes thus reducing the latency of reading data inputs. Moreover, Eley is equipped with a full delay operator to guarantee the performance of HPC applications - as they are running independently on a HPC system. The experimental results show the effectiveness of Eley in obtaining shorter execution time of Big Data applications (shorter map phase) while guaranteeing the performance of HPC applications.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
PublisherIEEE
Pages87-91
Number of pages5
ISBN (Electronic)9781538623268
ISBN (Print)9781538623275
DOIs
Publication statusPublished - 5 Sept 2017
Event2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 - Honolulu, United States
Duration: 5 Sept 20178 Sept 2017
https://ieeexplore.ieee.org/xpl/conhome/8048782/proceeding (Conference Proceedings)

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, CLUSTER
Volume2017-September
ISSN (Print)1552-5244
ISSN (Electronic)2168-9253

Conference

Conference2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
Country/TerritoryUnited States
CityHonolulu
Period5/09/178/09/17
Internet address

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

  • HPC
  • MapReduce
  • Big Data
  • Parallel File Systems
  • Burst Buffers
  • Interference
  • Prefetch

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